CN112417657B - Sponge city optimization design method based on different underlying surface pollutant contribution rates - Google Patents
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Abstract
The invention provides a sponge city optimization design method based on different underlying surface pollutant contribution rates, which comprises the following steps: establishing a basic data database of a target research area; constructing an SWMM model of a target research area according to the basic data database; based on the constructed SWMM model, simulating and calculating the load contribution rates of various non-point source pollutants of different underlying surfaces in a target research area, and determining the non-point source pollution characteristics of the various underlying surfaces; and (5) carrying out sponge city optimization design on the target research area. According to the invention, runoff pollutants on different underlying surfaces of a target research area are simulated based on the SWMM model, so that the non-point source pollutant load contribution rate of various underlying surfaces is obtained, the sponge city construction optimization design is performed according to the contribution rate in a targeted manner, and the effect of controlling the runoff pollutant source in the sponge city reconstruction project is achieved.
Description
Technical Field
The invention belongs to the technical field of sponge city construction, and particularly relates to a sponge city optimization design method based on different underlying surface pollutant contribution rates.
Background
In recent years, as urban construction is advanced, the underlying surface is changed, especially the area of the impermeable underlying surface such as a pavement, a roof and the like is greatly increased, the green area proportion is reduced, urban waterlogging is easy to cause when rainfall occurs, and rainfall runoff carries a large amount of pollutants to cause serious urban non-point source pollution (also called non-point source pollution).
The research shows that in a rainfall event, the scouring effects of different underlying surfaces on different pollutants are different, and the underlying surfaces are mainly divided into three types of roofs, pavements and greenbelts, and as roofing materials are mainly asphalt, the COD (chemical oxygen demand) and the SS concentration (suspended matter concentration) of the roofs are higher; due to the running of the vehicle and road materials, the road surface TN concentration (total nitrogen concentration) and COD concentration are high; green land SS concentration is higher. So the ratio of the underlying surface can directly influence the urban runoff pollution degree. The problems of unreasonable setting proportion among the waterproof pavement, the roof and the greening area and blind development and construction exist in the conventional urban planning and sponge urban transformation. By grasping the contribution rates of different understocks to pollutant transportation and reasonably planning the ratio of the understocks by using the contribution rates, effective technical guidance can be provided for urban planning and sponge urban transformation, so a pollutant contribution rate calculation method with rapid calculation and accurate results is urgently needed.
At present, the calculation method for the contribution rate of pollutants on different underlying surfaces mainly comprises the following steps: firstly, collecting the section pollutant flux of a monitoring point by a flux method, and calculating the contribution rate through a substance flux formula; secondly, calculating the contribution rate of pollutants by using a multivariate statistical method; thirdly, analyzing the contribution rate of pollutants by using a principal component analysis method.
However, the above method has problems that: the influence of rainfall scouring is ignored, the calculation result error is large, and the contribution rates of different underlying pollutants cannot be accurately reflected; because of different rainfall intensity and rainfall duration, the monitoring data needs to be updated continuously, so that the workload is high.
Disclosure of Invention
The embodiment of the invention aims to provide a sponge city optimization design method based on different contribution rates of pollutants on the underlying surface so as to overcome the technical defects.
In order to solve the technical problems, the invention provides a sponge city optimization design method based on different contribution rates of pollutants on the underlying surface, which comprises the following steps:
establishing a basic data database of a target research area;
constructing an SWMM model of a target research area according to the basic data database;
based on the constructed SWMM model, acquiring various non-point source pollutant load contribution rates of different underlying surfaces in a target research area, and determining non-point source pollution characteristics of various underlying surfaces;
and (5) carrying out sponge city optimization design on the target research area.
Further, the basic data database of the target research area is established, which specifically comprises:
acquiring a plane design drawing and a satellite image drawing of a target research area;
and dividing different underlying surface types in the target research area according to the plane design drawing and the satellite image drawing.
Further, based on the constructed model, the method further comprises the steps of:
obtaining measured flow data and measured water quality data of different underlying surfaces of the target research area;
calibrating hydrological parameters of the SWMM model according to the actually measured flow data;
and calibrating the water quality parameters of the SWMM model according to the actually measured water quality data.
Preferably, obtaining measured flow data of the target research area specifically includes:
monitoring the surface runoff flow of various underlying surfaces of the target research area and the flow of a key drainage pipe section;
meanwhile, monitoring the actual rainfall field of the target research area;
and continuously monitoring the key drainage pipe section, wherein the monitoring frequency is more than or equal to 15 min/time, and the number of the monitored rainfall fields is not less than three.
Further, the hydrological parameters of the SWMM model include pipe roughness, permeable zone manning coefficient, impermeable zone manning coefficient, maximum hypotonic coefficient, stable hypotonic coefficient, permeable zone depression volume, and impermeable zone depression volume.
Preferably, the obtaining measured water quality data of the target research area specifically includes:
setting surface runoff sampling points on various divided underlying surfaces in the target research area;
meanwhile, collecting water samples of flow monitoring points of key drainage pipe sections;
and sampling at the surface runoff sampling point and the critical pipe section flow monitoring point by using an automatic sampler or a manual collection mode at preset time points, wherein the number of water samples is more than or equal to 6, and respectively detecting the suspended matter concentration, the chemical oxygen demand concentration, the ammonia nitrogen concentration and the total phosphorus concentration of each water sample.
Further, the water quality parameters of the SWMM model include maximum cumulative amount, half saturation cumulative time, flush coefficient, and flush index.
Further, based on the constructed SWMM model, various non-point source pollutant load contribution rates of different underlying surfaces in the target research area are obtained, and various non-point source pollutant characteristics of various underlying surfaces are determined, which specifically comprises:
s1, establishing an SWMM model based on a target research area, and calibrating and verifying hydrologic water quality parameters of the whole target research area and various underlying surfaces through collected field rainfall, flow and water quality data of different underlying surfaces;
s2, based on the established SWMM model, sequentially utilizing the design rainfall conditions of the target research area to carry out rainfall simulation to obtain the total load of single non-point source pollutants on all the underlying surfaces of the target research area under the design rainfall conditions of different reproduction periodsP 0 ；
S3, sequentially canceling the same one of all the lower pad surfacesSetting parameters of non-point source-like pollutants, and re-performing rainfall simulation under the same design rainfall condition to obtain the total load of other non-point source-like pollutants on various underlying surfacesP i ；
S4, obtaining the load contribution rates of various non-point source pollutants on different underlying surfaces in the target research area according to the following formula:
wherein:a i a single-class non-point source pollutant load contribution rate for the i-th underlying surface,P 0 for the total loading of a single class of non-point source contaminants in the target study area,P i the total load of other non-point source pollutants of the ith underlying surface;
s5, determining various non-point source pollutant characteristics of various underlying surfaces according to various non-point source pollutant load contribution rates, wherein the method specifically comprises the following steps:
and determining the concentration change rule of various pollutants on different underlying surfaces of the target research area and the discharge amount of various pollution loads on different underlying surfaces of the target research area under different reproduction periods.
Further, the sponge city optimization design is carried out on the target research area, and the method specifically comprises the following steps:
under the constraint of the annual runoff total control rate, sponge city development measures are set in a targeted manner aiming at the pollution load contribution rates of different underlying surfaces.
Preferably, the base database includes target area plan, DEM elevation, pipe network lines, and rainfall data.
The beneficial effects of the invention are as follows:
according to the invention, various underlying surface pollutants in a target research area are simulated based on the SWMM model, so that the contribution rate of various underlying surface non-point source pollutants is obtained, sponge city control measures are optimized and laid according to the contribution rate in a targeted manner, and the purpose of improving the runoff pollutant source control effect of a sponge city transformation project is achieved.
In order to make the above-mentioned objects of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
FIG. 1 is a flow chart of a sponge city optimization design method based on different underlying surface contaminant contribution rates.
Detailed Description
Further advantages and effects of the present invention will become apparent to those skilled in the art from the disclosure of the present specification, by describing the embodiments of the present invention with specific examples.
In the invention, the upper, lower, left and right in the figures are regarded as the upper, lower, left and right of the sponge city optimization design method based on different contribution rates of the underlying surface pollutants.
The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the examples described herein, which are provided to fully and completely disclose the present invention and fully convey the scope of the invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, like elements/components are referred to by like reference numerals.
Unless otherwise indicated, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art. In addition, it will be understood that terms defined in commonly used dictionaries should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
First embodiment:
the embodiment relates to a sponge city optimization design method based on different contribution rates of pollutants on the underlying surface, which is shown in fig. 1 and comprises the following steps:
establishing a basic data database of a target research area;
constructing an SWMM model of the target research area according to the basic data database;
based on the constructed SWMM model, acquiring various non-point source pollutant load contribution rates of different underlying surfaces in the target research area, and determining non-point source pollution characteristics of the various underlying surfaces;
and carrying out sponge city optimization design on the target research area.
Wherein, a basic data base of the target research area can be established in ArcGIS software.
The construction of sponge city, i.e. the construction of low-influence rainwater development system, mainly means that through various technical approaches such as "seepage, stagnation, storage, purification, use, discharge", etc., benign hydrologic cycle of city is realized, the infiltration, regulation, purification, utilization and discharge capacity to runoff rainwater are improved, maintain or resume the sponge function of city, present sponge city construction often designs according to project topography, the non-point source pollution load discharge contribution rate of various underlying surfaces has been ignored, lead to sponge city to the non-point source pollutant removal rate inefficiency of city, and present city non-point source pollution condition is increasingly serious, must go on deeper understanding to regional non-point source pollution condition, in order to take effective pollution control measure.
In order to solve the problems, the invention adopts the SWMM model as a tool to simulate the rainfall condition of a target research area, and the SWMM (storm water management model, storm flood management model) is software developed by the United states Environmental Protection Agency (EPA) for simulating a dynamic rainfall-runoff process, and is widely applied to the aspect of rainfall runoff management at home and abroad.
The invention calculates the pollutant contribution rates of different underlying surfaces by using an SWMM model, and specifically comprises the following steps: the pollutant contribution rate of the underlying surface of the target research area under different rainfall intensities and different rainfall durations can be tracked and simulated, and the flexibility is high; the SWMM model comprises 4 calculation modules such as a runoff module, a conveying module, an expansion conveying module, a storage processing module and the like, and the accuracy of calculation results is high; reliable technical guidance is provided for urban planning and sponge urban transformation, so that the proportion planning of different underlying surfaces is more reasonable; after the model is established successfully, the rainfall recurrence period is input, so that the pollutant contribution rates of different underlying surfaces can be calculated rapidly, the operation is simple, and the popularization and the use are easy.
A base data database: the system comprises a target research area plan, a DEM elevation chart, a pipe network pipeline and rainfall data, but is not limited to the target research area plan, the DEM elevation chart, the pipe network pipeline and the rainfall data, the required data can be automatically adjusted according to the simulation purpose, and the acquisition of the data is a conventional technology in the field, can be obtained by looking up a table or obtaining by monitoring, and the like, and is not described in detail herein.
When the SWMM model is constructed, the obtained basic data are input into SWMM model software according to the requirements, and then the hydrologic water quality parameters of the whole target research area and various undersides are calibrated and verified through collected field rainfall, flow and water quality data of different undersides.
Based on the established SWMM model, rainfall is designed according to different reproduction periods under a local storm intensity formula to simulate rainfall on a target research area model, the total amount of various pollutant loads under the condition of different rainfall reproduction periods of the target research area is calculated, the load contribution rates of various non-point source pollutants on various underlying surfaces in the target research area are further calculated, the non-point source pollution characteristics of various underlying surfaces are determined, a sponge city construction scheme of the target research area is arranged in a targeted manner, and scientific basis is provided for urban non-point source pollution control and sponge city construction.
Second embodiment:
the embodiment relates to a sponge city optimization design method based on different underlying surface pollutant contribution rates, which comprises the following steps:
establishing a basic data database of a target research area;
acquiring a plane design drawing and a satellite image drawing of a target research area;
dividing different underlying surface types in a target research area according to a plane design drawing and a satellite image drawing;
constructing an SWMM model of the target research area according to the basic data database;
obtaining measured flow data and measured water quality data of different underlying surfaces of the target research area;
calibrating hydrological parameters of the SWMM model according to the actually measured flow data;
calibrating water quality parameters of the SWMM model according to the actually measured water quality data;
based on the constructed SWMM model, acquiring various non-point source pollutant load contribution rates of different underlying surfaces in the target research area, and determining non-point source pollution characteristics of the various underlying surfaces;
and carrying out sponge city optimization design on the target research area.
The different underlying surface types in the target research area are divided, so that the underlying surface in the target research area can be divided into four types, namely a roof, a road surface, a green land and undeveloped land;
compared to the first embodiment, this embodiment differs from the first embodiment in two points:
(1) The method comprises the steps of classifying the underlying surface of a target research area, dividing the underlying surface into four types of roofs, pavements, greenbelts and undeveloped lands according to a plane drawing and a satellite image drawing of the target research area, dividing and classifying different underlying surfaces through ArcGIS geographic information software (computer graphics application), forming different vector image layer data, and taking the vector image layer data as a basis for analyzing urban rainfall runoff and a confluence mechanism by a hydraulic model.
(2) After the SWMM model is constructed, parameter calibration is carried out, and the specific method is as follows:
the actual rainfall field runoff process, pollution process, total drainage flow and water quality of different underlying surfaces of a target research area are monitored, model parameters are calibrated and checked, namely, the model parameters are calibrated and checked by a manual trial-and-error method according to acquired actual measurement data, wherein the main calibration parameters are divided into hydrologic parameters and water quality parameters, and the hydrologic parameters comprise sensitive parameter value ranges such as pipeline roughness, permeable area Manning coefficient, impermeable area Manning coefficient, maximum infiltration coefficient, stable infiltration coefficient, permeable area depression volume, impermeable area depression volume and the like; the water quality parameters include maximum accumulation, half-saturation accumulation time, flush coefficient and flush index.
It should be noted that, the essence of parameter calibration is to firstly assume a group of parameters, substitute the parameters into a model to obtain a calculation result, then compare the calculation result with actual measurement data, and if the calculation value and the actual measurement value have little difference, take the parameter at this time as the parameter of the model; if the calculated value and the measured value have larger difference, the adjustment parameters are substituted into the model to be recalculated, and then the comparison is carried out until the error between the calculated value and the measured value meets a certain range.
In this application, rating parameters are model hydrologic parameters and model water quality parameters.
The actually measured flow data specifically includes:
monitoring the surface runoffs of various underlying surfaces of the target research area and the flow of key drainage pipe sections;
meanwhile, monitoring the actual rainfall field of the target research area;
continuously monitoring all key nodes of a pipe network, wherein the monitoring frequency is more than or equal to 15 min/time, and the number of monitored rainfall fields is not less than three;
when actually measuring flow data, the design rainfall standard and the corresponding annual runoff total control rate of the facility can be judged according to the connection relation of the rainwater facilities in the target research area and the water collection area by combining the related design drawing of the target research area with site survey.
The measured water quality parameters specifically comprise:
setting surface runoff sampling points on various divided underlying surfaces in the target research area;
meanwhile, collecting water samples of flow monitoring points of key drainage pipe sections;
and sampling at the surface runoff sampling point and the critical pipe section flow monitoring point by using an automatic sampler or a manual collection mode at preset time points, wherein the number of water samples is more than or equal to 6, and respectively detecting the suspended matter concentration, the chemical oxygen demand concentration, the ammonia nitrogen concentration and the total phosphorus concentration of each water sample.
In order to clearly describe the process of obtaining the measured water quality parameters, the following will be exemplified by specific numerical values:
aiming at different underlying surfaces and key nodesArranging surface runoff sampling points, and sampling at intervals of 0min, 5min, 10min, 20min, 30min, 60min, 90min and 120min after runoff occurs in an automatic sampler or manual collection mode, wherein the volume of each sample is about 500ml; if the rainfall duration is longer, the later sampling quantity can be properly increased, and the sampling interval can be properly prolonged; if the rainfall duration is short, the sampling quantity can be properly reduced, but the water quality detection project comprises Suspended Substances (SS), chemical Oxygen Demand (COD), ammonia Nitrogen (NH) 3 -N), total Phosphorus (TP), etc.
Modeling:
in the sub-catchment area division and pipe network generalization process: classifying the underlying surfaces of the target research areas based on the collected data, dividing the catchment areas by using ArcGIS software, and calculating parameters such as the areas of the catchment areas, the characteristic widths, the gradients, the percentages of the impermeable surfaces, the areas of different underlying surfaces and the like; importing data such as pipe network of a research area, elevation in the inner bottom of a node, length of a pipe section and the like into ArcGIS software, and outputting the data as inp files; in SWMM software, after other parameters are set according to manual and design data, corresponding SS, COD, NH is set 3 And inputting parameters required by accumulation and scouring of the ground surface objects according to main pollution factors such as N, TN, TP and the like, wherein the accumulation parameters of the ground surface objects comprise a maximum accumulation amount and accumulation time, and the scouring parameters comprise a scouring coefficient and a scouring index.
According to the implementation mode, the SWMM model is used for carrying out simulation evaluation on the surface runoff of the target research area, and various rainwater partition areas and characteristic widths calculated by ArcGIS are input into the SWMM model by combining the existing data and the underlying surface analysis result; selecting a motion wave model by a pipeline transmission algorithm model of the model; selecting a Horton model from the infiltration model; the pipeline data are set according to the pipeline data of the research area; the contaminant model selects a saturation function as a process of accumulating contaminants in non-rain and rain phases.
Third embodiment:
the embodiment relates to a sponge city optimization design method based on different contribution rates of pollutants on the underlying surface, which is shown in fig. 1 and comprises the following steps:
establishing a basic data database of a target research area;
constructing an SWMM model of the target research area according to the basic data database;
based on the constructed SWMM model, acquiring various non-point source pollutant load contribution rates of different underlying surfaces in the target research area, and determining non-point source pollution characteristics of the various underlying surfaces;
and carrying out sponge city optimization design on the target research area.
Based on the constructed SWMM model, the load contribution rates of various non-point source pollutants of different underlying surfaces in a target research area are obtained, and the characteristics of various non-point source pollutants of various underlying surfaces are determined, which specifically comprises the following steps:
s1, establishing an SWMM model based on a target research area, and calibrating and verifying hydrologic water quality parameters of the whole target research area and various underlying surfaces through collected field rainfall, flow and water quality data of different underlying surfaces;
s2, based on the established SWMM model, sequentially utilizing the design rainfall conditions of the target research area to carry out rainfall simulation to obtain the total load of single non-point source pollutants on all the underlying surfaces of the target research area under the design rainfall conditions of different reproduction periodsP 0 ；
S3, sequentially canceling the setting parameters of the same type of non-point source pollutants on all the underlying surfaces, and re-performing rainfall simulation under the same design rainfall condition to obtain the total load of the other types of non-point source pollutants on all the underlying surfacesP i ；
S4, obtaining the load contribution rates of various non-point source pollutants on different underlying surfaces in the target research area according to the following formula:
wherein:a i a single-class non-point source pollutant load contribution rate for the i-th underlying surface,P 0 total loading of single class of non-point source contaminants for a target study area，P i The total load of other non-point source pollutants of the ith underlying surface;
s5, determining various non-point source pollutant characteristics of various underlying surfaces according to various non-point source pollutant load contribution rates, wherein the method specifically comprises the following steps:
and determining the concentration change rule of various pollutants on different underlying surfaces of the target research area and the discharge amount of various pollution loads on different underlying surfaces of the target research area under different reproduction periods.
In the steps, based on the established model, sequentially applying a local typical rain model to carry out rainfall simulation on a target research area, and calculating the total load amount of various pollutants in the target research area under typical rainfall conditions; and then sequentially canceling pollutant setting parameters of various understock surfaces, re-simulating the model under the condition of the same rainfall reproduction period, and calculating the total amount of various pollutant loads, wherein the front-back difference value is used as the contribution of a certain type of understock to the total pollution load of a target research area.
The sponge city construction scheme for laying the target research area specifically comprises the following steps:
under the constraint of the annual runoff total control rate, sponge city development measures are set in a targeted manner aiming at the pollution load contribution rates of different underlying surfaces.
For example, the contribution rate of road pollutants is higher, the number of road sponge city measures is increased pertinently in the established SWMM model, simulation is carried out under the same design reproduction period, and whether the established sponge city measures are effective or not is judged through simulation results, so that the aim of effectively controlling non-point source pollution of the road is achieved.
The non-point source pollution characteristics of various underlying surface runoffs in the target research area can be provided visually by the various pollutant contribution rates of various underlying surfaces obtained through the calculation method, and the non-point source pollution conditions of the local rainfall runoffs can be relieved more directly and effectively by analyzing the various pollutant contribution rates of different underlying surfaces and combining with the corresponding LID measures which can be designed in a targeted manner according to the local actual conditions.
According to the invention, through accurate monitoring and data simulation of the surface runoff of the research area, the pollution contribution rate of various pollutants in different underlying surfaces to the whole rainfall runoff of the research area is calculated, and the non-point source pollution characteristics of the area are determined, so that a sponge city construction scheme is designed in a targeted manner. For urban construction, the traditional sponge urban construction often emphasizes that the radial flow control rate is used as the basis of the sponge urban construction, and the threat of non-point source pollution of rainfall runoff in a research area to the urban integral water environment is ignored, so that the existing sponge urban construction scheme has poor treatment effect on the non-point source pollution of the rainfall runoff. The non-point source pollution conditions in the urban areas are increasingly serious, and more thorough understanding of the non-point source pollution conditions in the areas is required to take effective pollution control measures. The sponge city construction method for researching the contribution rates of pollutants on different underlying surfaces of the region can better meet the requirement of non-point source pollution control of the region. The invention has multiple meanings for researching urban sponge city construction based on the actual requirement of non-point source pollution control in urban areas and the requirement of non-point source pollution simulation research development. The simulation result can provide scientific basis for urban non-point source pollution control and sponge urban construction.
Fourth embodiment:
according to the sponge city optimization design method based on different underlying surface pollutant contribution rates, the following implementation is carried out in a certain research area:
taking a district in the western security city as a research object, and dividing the underlying surface of the research area into three categories of a roof, a road and a green land based on the land type of the research area;
monitoring and collecting flow water quality data of different underlying monitoring points, and calibrating and verifying the model through monitored scene rainfall data;
the results show that the parameters of the model are set within the error range and can be used for establishing the model.
Setting rainfall recurrence periods to be p=0.5a, p=1a, p=3a and p=5a respectively by using a stormwater intensity formula in the western city, wherein P is the recurrence period and a is the year;
simulating the established SWMM model by rainfall in different design reproduction periods, and finally obtaining various underlying surface pollution contribution rates in different reproduction periods through calculation;
the results show that:
the pollution contribution rate of the roof of the research area is the largest, and secondly, the road and the green land are respectively 62.74 percent, 32.47 percent and 4.79 percent of the SS average pollution contribution rate of the road and the green land of the roof to the research area; the average pollution contribution rate of COD is 52.56%, 39.82% and 7.62% respectively; NH (NH) 3 The average pollution contribution rate of N is 65.95%, 21.78% and 12.27%, respectively; average contamination contribution rates of TP were 59.58%, 28.75%, 11.67%, respectively;
through simulation results, measures such as green roofs and rain buckets in research areas are increased in a targeted manner, so that the roof pollution contribution rate is effectively reduced, and measures such as water-permeable pavement, grass planting ditches and the like can be added to roads to effectively reduce the road pollution contribution rate.
It will be understood by those of ordinary skill in the art that the foregoing embodiments are specific examples of carrying out the invention and that various changes in form and details may be made therein without departing from the spirit and scope of the invention.
Claims (8)
1. The sponge city optimization design method based on different underlying surface pollutant contribution rates is characterized by comprising the following steps:
step S1, a basic data database of a target research area is established;
s2, constructing an SWMM model of the target research area according to the basic data database;
step S3, based on the constructed SWMM model, obtaining the load contribution rates of various non-point source pollutants of different underlying surfaces in the target research area, and determining the non-point source pollution characteristics of the underlying surfaces of the various types, wherein the method specifically comprises the following steps:
s301, establishing an SWMM model based on a target research area, and calibrating and verifying hydrologic water quality parameters of the whole target research area and various underlying surfaces through collected field rainfall, flow and water quality data of different underlying surfaces;
s302, based on the established SWMM model, sequentially utilizing the design rainfall conditions of the target research area to perform rainfall simulation to obtainTotal loading of single class of non-point source contaminants for all underlying surfaces of the target study area under design rainfall conditions at different recurring periodsP 0 ；
S303, sequentially canceling the setting parameters of the same type of non-point source pollutants on all the underlying surfaces, and re-performing rainfall simulation under the same design rainfall condition to obtain the total load of the other types of non-point source pollutants on all the underlying surfacesP i ；
S304, acquiring load contribution rates of various non-point source pollutants on different underlying surfaces in a target research area according to the following formula:
wherein:a i a single-class non-point source pollutant load contribution rate for the i-th underlying surface,P 0 for the total loading of a single class of non-point source contaminants in the target study area,P i the total load of other non-point source pollutants of the ith underlying surface;
s305, determining various non-point source pollutant characteristics of various underlying surfaces according to various non-point source pollutant load contribution rates, wherein the method specifically comprises the following steps:
determining the concentration change rule of various pollutants on different underlying surfaces of the target research area and the emission of various pollution loads on different underlying surfaces of the target research area under different reproduction periods;
step S4, sponge city optimization design is carried out on the target research area, and the method specifically comprises the following steps:
under the constraint of the annual runoff total control rate, sponge city development measures are set in a targeted manner aiming at the pollution load contribution rates of different underlying surfaces.
2. The sponge city optimization design method based on different underlying surface pollutant contribution rates as claimed in claim 1, wherein the building of the basic data database of the target research area specifically comprises:
acquiring a plane design drawing and a satellite image drawing of the target research area;
and dividing different underlying surface types in the target research area according to the plane design drawing and the satellite image drawing.
3. The sponge city optimization design method based on different underlying surface pollutant contribution rates according to claim 1 or 2, wherein based on the constructed SWMM model, obtaining various non-point source pollutant load contribution rates of various underlying surfaces in the target research area, and before determining various non-point source pollutant characteristics of various underlying surfaces, further comprises:
obtaining measured flow data and measured water quality data of different underlying surfaces of the target research area;
calibrating hydrologic parameters of the SWMM model according to the actually measured flow data;
and calibrating the water quality parameters of the SWMM model according to the actually measured water quality data.
4. The sponge city optimization design method based on different underlying surface pollutant contribution rates as claimed in claim 3, wherein obtaining measured flow data of a target research area comprises the following steps:
monitoring the surface runoff flow of various underlying surfaces of the target research area and the flow of a key drainage pipe section;
meanwhile, monitoring the actual rainfall field of the target research area;
and continuously monitoring the key drainage pipe section, wherein the monitoring frequency is more than or equal to 15 min/time, and the number of the monitored rainfall fields is not less than three.
5. The sponge city optimization design method based on different underlying surface contaminant contribution rates according to claim 4, wherein the hydrologic parameters of the SWMM model include pipeline roughness, permeable zone manning coefficient, impermeable zone manning coefficient, maximum infiltration coefficient, stable infiltration coefficient, permeable zone depression volume, and impermeable zone depression volume.
6. The sponge city optimization design method based on different underlying surface pollutant contribution rates as claimed in claim 3, wherein obtaining measured water quality data of a target research area comprises the following steps:
setting surface runoff sampling points on various divided underlying surfaces in the target research area;
meanwhile, collecting water samples of flow monitoring points of key drainage pipe sections;
and sampling at the surface runoff sampling point and the critical pipe section flow monitoring point by using an automatic sampler or a manual collection mode at preset time points, wherein the number of water samples is more than or equal to 6, and respectively detecting the suspended matter concentration, the chemical oxygen demand concentration, the ammonia nitrogen concentration and the total phosphorus concentration of each water sample.
7. The sponge city optimization design method based on different underlying surface contaminant contribution rates according to claim 6, wherein the water quality parameters of the SWMM model include maximum cumulative amount, half-saturated cumulative time, flush coefficient, and flush index.
8. The sponge city optimization design method based on different underlying surface contaminant contribution rates according to claim 1, wherein said base data database comprises said target study area plan, DEM elevation, pipe network line, and rainfall data.
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